An application of fuzzy TOPSIS on ranking products: A case study of faucet devices

Kia, Seyed Hasan; Danaei, Abolfazl; Oroei, Mehrdad

doi:10.5267/j.dsl.2013.08.004

Cited by 8 publications

(5 citation statements)

References 15 publications

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“…The fuzzy TOPSIS method was proposed by Chen (2000). An example of applying this method can be found, among others, in the studies of: Chang and Tseng (2008), Uyun and Riadi (2011), Madi and Tap (2011), Yayla et al (2012), Jannatifar et al (2012), Erdoğan et al (2013), Ataei (2013) and Kia et al (2014). The major difference between the fuzzy TOPSIS method and the original one is that fuzzy numbers are used in the computation of the rankings and the performance scores of the observations.…”

Section: Methodsmentioning

confidence: 99%

Efficiency in South African agriculture: a two-stage fuzzy approach

Aye

Gupta

2018

BIJ

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Purpose The purpose of this paper is to assess the efficiency of agricultural production in South Africa from 1970 to 2014, using an integrated two-stage fuzzy approach. Design/methodology/approach Fuzzy technique for order preference by similarity to ideal solution is used to assess the relative efficiency of agriculture in South Africa over the course of the years in the first stage. In the second stage, fuzzy regressions based on different rule-based systems are used to predict the impact of socio-economic and demographic variables on agricultural efficiency. They are compared with the bootstrapped truncated regressions with conditional α levels proposed in Wanke et al. (2016a). Findings The results show that the fuzzy efficiency estimates ranged from 0.40 to 0.68 implying inefficiency in South African agriculture. The results further reveal that research and development, land quality, health expenditure–population growth ratio have a significant, positive impact on efficiency levels, besides the GINI index. In terms of accuracy, fuzzy regressions outperformed the bootstrapped truncated regressions with conditional α levels proposed in Wanke et al. (2015). Practical implications Policies to increase social expenditure especially in terms of health and hence productivity should be prioritized. Also policies aimed at conserving the environment and hence the quality of land is needed. Originality/value The paper is original and has not been previously published elsewhere.

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Section: Methodsmentioning

confidence: 99%

Efficiency in South African agriculture: a two-stage fuzzy approach

Aye

Gupta

2018

BIJ

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“…The difference in relation to the primary version of this method is in expressing assessments and/or criteria's weights in the form of triangular fuzzy numbers. An example of applying this method can be found, among others, in the studies of: Chang and Tseng [2008], Uyun and Riadi [2011], Madi and Tap [2011], Yayla et al [Yayla, Yildiz, Özbek 2012], Jannatifar et al [Jannatifar, Shahi, Moradi 2012], Erdoğan et al [2013], Ataei [2013], Kia et al [Kia, Danaei, Oroei 2014].…”

Section: Fuzzy Topsis Methodsmentioning

confidence: 99%

The fuzzy TOPSIS method and its implementation in the R programme

Dudek¹,

Jefmański²

2015

Informatyka Ekonomiczna

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The TOPSIS method (Technique for Order Preference by Similarity Ideal Solution) suggested by Hwang and Yoon [1981], belongs to the group of pattern linear ordering methods of multidimensional objects. A characteristic feature of this method is a way to evaluate a synthetic criterion's values, which takes into consideration the distance of an evaluated object from a positive-ideal solution as well as from a negative-ideal solution. The fuzzy TOPSIS method enables the linear ordering of objects described through linguistic variables, whose values are expressed in the form of triangular fuzzy numbers. In this article, a way of synthetic measurement estimation in environment R was presented, according to the assumptions of the fuzzy TOPSIS method proposed by Chen [2000]. Scripts, which are included in the article make the accomplishment of this particular method's stages possible.

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“…Kia et al used fuzzy-based TOPSIS to rank various alternative productions in the faucet industry under uncertain conditions, where five criteria of profitability, life cycle, quality, social issues, and production capacity were taken into account [61]. Khemiri et al proposed a framework for integrating procurement/production (IPP) in a multi-product SCM.…”

Section: Topsismentioning

confidence: 99%

A Comprehensive Fuzzy Decision-Making Method for Minimizing Completion Time in Manufacturing Process in Supply Chains

et al. 2021

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In manufacturing firms, there are many factors that can affect product completion time in production lines. However, in a real production environment, such factors are uncertain and increase the adverse effects on product completion time. This research focuses on the role of internal factors in small- and medium-scale supply chains in developing countries, enhancing product completion time during the manufacturing process in fuzzy conditions. In the first step of this research, a list of factors was found clustered into six main groups: technology, human resources, machinery, material, facility design, and social factors. In the next step, fuzzy weights of each group factor were determined by a fuzzy inference system to reflect the uncertainty of the factors in utilizing product completion time. Then, a hybrid fuzzy–TOPSIS-based heuristic is proposed to generate and select the best production alternative. The outcomes showed that the proposed method could generate and select the alternative with a 10.13% lower product completion time. The findings also indicated that using the proposed fuzzy method will cause less minimum variance compared to the crisp mode.

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An application of fuzzy TOPSIS on ranking products: A case study of faucet devices

Cited by 8 publications

References 15 publications

Efficiency in South African agriculture: a two-stage fuzzy approach

Efficiency in South African agriculture: a two-stage fuzzy approach

The fuzzy TOPSIS method and its implementation in the R programme

A Comprehensive Fuzzy Decision-Making Method for Minimizing Completion Time in Manufacturing Process in Supply Chains

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